Context-directed segmentation algorithm for handwritten numeral strings
Image and Vision Computing
Handwritten numerical recognition based on multiple algorithms
Pattern Recognition
Class-based n-gram models of natural language
Computational Linguistics
Incorporating Language Syntax in Visual Text Recognition with a Statistical Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
A High Performance Hand-printed Numeral Recognition System with Verification Module
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Hybrid Classifier for Recognizing Handwritten Numerals
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Methodology for Deriving Probabilistic Correctness Measures from Recognizers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
Structural hidden Markov models: An application to handwritten numeral recognition
Intelligent Data Analysis
Structural hidden Markov models based on stochastic context-free grammars
Control and Intelligent Systems
Probabilistic logic with minimum perplexity: Application to language modeling
Pattern Recognition
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This paper presents Nonstationary Markovian Models and their application to recognition of strings of tokens. Domain specific knowledge is brought to bear on the application of recognizing zip Codes in the U.S. mailstream by the use of postal directory files. These files provide a wealth of information on the delivery points (mailstops) corresponding to each zip code. This data feeds into the models as n-grams, statistics that are seamlessly integrated with recognition scores of digit images. An especially interesting facet of the model is its ability to excite and inhibit certain positions in the n-grams leading to the familiar area of Markov Random Fields. The authors have previously described elsewhere [2] a methodology for deriving probability values from recognizer scores. These probability measures allow the Markov chain to be constructed in a truly Bayesian framework. We empirically illustrate the success of Markovian modeling in postprocessing applications of string recognition. We present the recognition accuracy of the different models on a set of 20,000 zip codes. The performance is superior to the present system which ignores all contextual information and simply relies on the recognition scores of the digit recognizers.